Pupfish-LLC

Performance optimization for Qlik Sense: three-metric framework (Base RAM, Peak RAM, Reload Time), key encoding hierarchy, QVD optimized load rules, expression performance (If vs set analysis, Aggr caution, master items for caching), memory choreography, and data reduction techniques. Load when optimizing or reviewing performance-sensitive artifacts.

Complete QA checklist used by the qa-reviewer agent for validating Qlik development artifacts. Covers data model integrity, naming convention compliance, script quality, expression correctness, security gaps, cross-artifact consistency, blocked dependency audit, and data quality validation. Also available for manual invocation outside the pipeline via /qlik-review-checklist.

Section access patterns, row-level security, data reduction, OMIT field usage, hybrid security models, and Cloud vs. client-managed security differences. Use when designing or reviewing security configurations.

Initialize a new Qlik project with standard directory structure, input subdirectories, artifact phases, and dependency tracking template.

Post-load data quality validation patterns for Qlik development. Provides query templates for null rate analysis, referential integrity checks, value distribution analysis, row count validation, orphaned record detection, sparse field identification, and duplicate detection. Usable by the qa-reviewer when MCP or post-load data access is available. Also provides patterns for embedding validation checks directly into load scripts. Load when performing data quality validation or writing diagnostic scripts.

Captures existing platform patterns for brownfield Qlik projects. Provides structured templates for documenting existing app inventory, shared subroutine catalogs, naming convention maps, data connection standards, QVD storage conventions, and organizational coding standards. Used by requirements-analyst during Phase 0 context ingestion and by script-developer during Phase 4 for platform compatibility. Load when ingesting platform context or writing scripts that must integrate with existing platform conventions.

Capability registry for the Qlik Cloud MCP server. Maps MCP tools to pipeline phases, provides MCP detection patterns, documents behavioral gotchas not covered by tool definitions, and defines multi-step workflows for expression validation, reference app analysis, visualization scaffolding, and data quality checks. Load whenever an agent needs to interact with a live Qlik Cloud tenant. The tool definitions themselves document parameters, response structures, and basic usage -- this skill covers framework integration, sequencing, and pitfalls discovered through live testing.

Star schema patterns, key resolution strategies, synthetic key prevention, QVD layer design, multi-app architecture patterns, source architecture consumption strategies, and associative engine behavior for Qlik Sense data modeling. Load when designing or reviewing data models.

Post-load data quality validation patterns for Qlik development. Provides query templates for null rate analysis, referential integrity checks, value distribution analysis, row count validation, orphaned record detection, sparse field identification, and duplicate detection. Usable by the qa-reviewer when MCP or post-load data access is available. Also provides patterns for embedding validation checks directly into load scripts. Load when performing data quality validation or writing diagnostic scripts.

Captures existing platform patterns for brownfield Qlik projects. Provides structured templates for documenting existing app inventory, shared subroutine catalogs, naming convention maps, data connection standards, QVD storage conventions, and organizational coding standards. Used by requirements-analyst during Phase 0 context ingestion and by script-developer during Phase 4 for platform compatibility. Load when ingesting platform context or writing scripts that must integrate with existing platform conventions.

Star schema patterns, key resolution strategies, synthetic key prevention, QVD layer design, multi-app architecture patterns, source architecture consumption strategies, and associative engine behavior for Qlik Sense data modeling. Load when designing or reviewing data models.

Deployment procedures for Qlik Sense Cloud and client-managed environments. Covers app import, data connection setup, task scheduling, security configuration, and environment-specific considerations. Invoke manually with /qlik-deploy when preparing deployment artifacts or runbooks.

Set analysis syntax and patterns, aggregation functions, TOTAL qualifier usage, Aggr() patterns, conditional expressions, null handling in expressions, dollar-sign expansion, expression performance optimization, and common anti-patterns for Qlik Sense expression development. Load when writing or reviewing Qlik expressions.

Script syntax reference, QVD optimization, incremental load patterns (insert-only, insert/update, insert/update/delete, dual-timestamp for SCD2), master calendar generation, variable definitions, error handling, logging patterns, null handling patterns, diagnostic and validation patterns, subroutine integration, and platform gotchas (SET vs LET, dollar-sign expansion timing, SET variable comma limitation). Load when writing or reviewing Qlik load scripts.

Field naming, variable naming, table naming, expression naming, and file naming standards for Qlik Sense development. Includes cross-layer naming strategy for tracing field names from source through every ETL layer to UI display name. Includes prefix/suffix conventions, reserved words, and correct vs. incorrect examples. Load when writing or reviewing any Qlik artifact.

Memory optimization, script load optimization, expression calculation optimization, calculation conditions, data reduction techniques, and profiling/diagnostic approaches for Qlik Sense. Load when optimizing or reviewing performance-sensitive artifacts.

Script syntax reference, QVD optimization, incremental load patterns (insert-only, insert/update, insert/update/delete, dual-timestamp for SCD2), JOIN/KEEP prefixes, ApplyMap patterns, CROSSTABLE, master calendar generation, variable definitions, error handling, logging patterns, null handling patterns, diagnostic and validation patterns, subroutine integration, and platform gotchas (SET vs LET, dollar-sign expansion timing, SET variable comma limitation). Load when writing, reviewing, or debugging Qlik load scripts, QVD operations, STORE/LOAD syntax, preceding LOAD, NullAsValue, script organization, JOIN, KEEP, ApplyMap, CROSSTABLE, AutoNumber, composite keys, or data quality defensive coding.

Catalog of failure classes that appear in Qlik Sense development artifacts, with severity model and finding-format contract. Covers script syntax errors, data model integrity issues (synthetic keys, circular references, grain misalignment), naming convention violations, expression correctness gaps, security misconfigurations (Section Access, PII), cross-artifact inconsistency, blocked dependency tracking, and post-load data quality. Load when reviewing a Qlik artifact (data model spec, load script, expression catalog, visualization spec, or full app), running a QA pass before sign-off, classifying defect severity, formatting findings for remediation, or deciding which checks apply to a given review scope.

Set analysis syntax and patterns ({1}, {$}, element-set arithmetic, P()/E(), $(=...) modifiers), aggregation functions, TOTAL qualifier usage, Aggr() patterns, conditional expressions, null handling in expressions, dollar-sign expansion timing, expression performance optimization, calculation conditions, and common Qlik Sense expression anti-patterns. Load when writing, reviewing, or debugging Qlik expressions — master measures, master dimensions, calculated dimensions, KPI formulas, set-analysis snippets, year-over-year and rolling-window calculations, or any expression that misbehaves under selection.

Star schema design, key resolution (natural/composite/hash/AutoNumber), synthetic key prevention, circular reference resolution, QVD layer architecture, multi-app patterns (single app, generator/consumer, extract-transform-model-UI, binary load), source architecture consumption (dimensional warehouse, OLTP, Data Vault 2.0, pre-joined views, flat files), grain alignment across multiple facts, bridge tables, link tables, ApplyMap vs dimension, HidePrefix/HideSuffix. Load when designing or reviewing a Qlik Sense data model, deciding between single-app and multi-app, choosing a key strategy, or diagnosing synthetic keys and circular references.

This skill should be used when naming, renaming, prefixing, aliasing, or mapping any Qlik Sense field, table, variable, or expression. Covers entity-prefix dot notation for non-key fields, key field conventions (% prefix, _key suffix, HidePrefix, HideSuffix), table naming (dimension, fact, temp, mapping, bridge), variable naming (v prefix, field-reference vs expression variables, variable-to-expression name mirroring), expression naming (master measures, master dimensions, calculated dimensions), script and QVD file naming, the cross-layer field naming strategy from source through extract/transform/model to UI, the Mapping RENAME field-rename layer, variable-to-field-name alignment, and the most common Qlik naming anti-patterns (silent synthetic keys, inconsistent key names, double-prefix from QUALIFY).

Qlik project directory layout convention for Claude-Code workflows: data-sources, scripts, qvds, documentation, tests directories with per-directory rationale and starter README templates. Cross-platform PowerShell + Bash scaffolding scripts that are idempotent (re-running preserves existing files). Load when starting a new Qlik Sense project, setting up a workspace for Claude to help with, or evaluating an existing project's directory layout against a baseline convention.

Post-load data quality validation patterns for Qlik Sense. Provides query templates for null-rate analysis, referential integrity checks, value distribution analysis, row-count validation, orphaned-record detection, sparse-field identification, and duplicate-key detection — usable against a live tenant via MCP or any post-load data interface. Also provides patterns for embedding validation checks directly into load scripts so failures surface during reload rather than in production. Load when running data quality checks against a loaded app, writing diagnostic or validation scripts, or designing fail-fast assertions inside a load script.

Brownfield Qlik platform discovery: subroutine identification and limitation patterns from existing .qvs scripts, naming-convention variations (entity-prefix dot vs underscore vs camelCase; dim_/fact_ vs hub_/sat_/lnk_ vs Map_), connection conventions, QVD storage conventions (centralized layered vs per-app vs date-stamped), architecture pattern detection (single-app, generator/consumer, four-layer, binary load), incremental load detection, and convention-conflict resolution when platform conventions differ from framework defaults. Load when working in a brownfield Qlik environment, reverse-engineering an existing platform's conventions, or integrating new development with shared subroutine libraries.